{"instruction": "You are an expert in event extraction. Please extract events from the input that conform to the schema definition. Return an empty list for events that do not exist, and return NAN for arguments that do not exist. If an argument has multiple values, please return a list. Respond in the format of a JSON string.", "schema": [{"event_type": "data breach", "trigger": true, "arguments": ["victim", "damage amount", "tool", "compromised data", "number of victim", "number of data", "purpose", "attack pattern", "place", "time"]}, {"event_type": "patch vulnerability", "trigger": true, "arguments": ["vulnerable system", "vulnerability", "issues addressed", "patch number", "vulnerable system version", "releaser"]}, {"event_type": "phishing", "trigger": true, "arguments": ["place", "attacker", "damage amount", "purpose", "trusted entity", "attack pattern", "time", "tool", "victim"]}, {"event_type": "ransom", "trigger": true, "arguments": ["place", "time", "price", "attacker", "payment method", "victim", "tool"]}, {"event_type": "discover vulnerability", "trigger": true, "arguments": ["supported platform", "common vulnerabilities and exposures", "vulnerable system", "vulnerability", "time", "discoverer", "vulnerable system owner", "capabilities", "vulnerable system version"]}], "input": "⬭ LinkedIn message notifications that tried to trick users into giving away LinkedIn creds ."}
{"data breach": [], "patch vulnerability": [], "phishing": [{"trigger": "trick", "arguments": {"place": "NAN", "attacker": "NAN", "damage amount": "NAN", "purpose": "giving away LinkedIn creds", "trusted entity": "NAN", "attack pattern": "NAN", "time": "NAN", "tool": "LinkedIn message notifications", "victim": "users"}}], "ransom": [], "discover vulnerability": []}